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app.py
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app.py
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from flask import Flask, render_template, request
import jsonify
import requests
import pickle
import numpy as np
import sklearn
from sklearn.preprocessing import StandardScaler
app = Flask(__name__)
model = pickle.load(open('random_forest_regression_model.pkl', 'rb'))
@app.route('/', methods=['GET'])
def Home():
return render_template('index.html')
standard_to = StandardScaler()
@app.route("/predict", methods=['POST'])
def predict():
Fuel_Type_Diesel = 0
if request.method == 'POST':
Year = int(request.form['Year'])
Present_Price = float(request.form['Present_Price'])
Kms_Driven = int(request.form['Kms_Driven'])
Kms_Driven2 = np.log(Kms_Driven)
Owner = int(request.form['Owner'])
Fuel_Type_Petrol = request.form['Fuel_Type_Petrol']
if(Fuel_Type_Petrol == 'Petrol'):
Fuel_Type_Petrol = 1
Fuel_Type_Diesel = 0
elif(Fuel_Type_Petrol == 'Diesel'):
Fuel_Type_Petrol = 0
Fuel_Type_Diesel = 1
else :
Fuel_Type_Petrol = 0
Fuel_Type_Diesel = 0
Year = 2020-Year
Seller_Type_Individual = request.form['Seller_Type_Individual']
if(Seller_Type_Individual == 'Individual'):
Seller_Type_Individual = 1
else:
Seller_Type_Individual = 0
Transmission_Mannual = request.form['Transmission_Mannual']
if(Transmission_Mannual == 'Mannual'):
Transmission_Mannual = 1
else:
Transmission_Mannual = 0
prediction = model.predict([[Present_Price, Kms_Driven2, Owner, Year, Fuel_Type_Diesel,
Fuel_Type_Petrol, Seller_Type_Individual, Transmission_Mannual]])
output = round(prediction[0], 2)
if output < 0:
return render_template('index.html', prediction_texts="Sorry you cannot sell this car")
else:
return render_template('index.html', prediction_text="You can sell the Car at ₹{} Lakhs".format(output))
else:
return render_template('index.html')
if __name__ == "__main__":
app.run(debug=True)